diff --git a/planning/planning_debug_tools/CMakeLists.txt b/planning/planning_debug_tools/CMakeLists.txt
index 389bfdaa..0fd805f4 100644
--- a/planning/planning_debug_tools/CMakeLists.txt
+++ b/planning/planning_debug_tools/CMakeLists.txt
@@ -52,6 +52,7 @@ install(PROGRAMS
   scripts/trajectory_visualizer.py
   scripts/closest_velocity_checker.py
   scripts/perception_replayer/perception_reproducer.py
+  scripts/perception_replayer/perception_reproducer_v2.py
   scripts/perception_replayer/perception_replayer.py
   scripts/update_logger_level.sh
   DESTINATION lib/${PROJECT_NAME}
diff --git a/planning/planning_debug_tools/scripts/perception_replayer/perception_reproducer_v2.py b/planning/planning_debug_tools/scripts/perception_replayer/perception_reproducer_v2.py
new file mode 100644
index 00000000..dd445b92
--- /dev/null
+++ b/planning/planning_debug_tools/scripts/perception_replayer/perception_reproducer_v2.py
@@ -0,0 +1,235 @@
+#!/usr/bin/env python3
+
+# Copyright 2023 TIER IV, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import pickle
+from collections import deque
+
+import numpy as np
+from perception_replayer_common import PerceptionReplayerCommon
+import rclpy
+from utils import StopWatch
+from utils import create_empty_pointcloud
+from utils import translate_objects_coordinate
+
+dist_eps = 1e-2 # (meters)
+
+class PerceptionReproducerV2(PerceptionReplayerCommon):
+    '''
+    PerceptionReproducerV2 is used reproduces perception topic data from rosbag, specifically optimized for use with simulators.
+    '''
+    def __init__(self, args):
+        self.rosbag_ego_odom_seach_radius = args.seach_radius # (m) the range of the ego odom to search, 
+        self.reproduce_cooldown = args.reproduce_cooldown # (sec) the cooldown time for republishing published data, please make sure that it's greater than the ego's stopping time. 
+        
+        super().__init__(args, "perception_reproducer_v2")
+        
+        self.reproduce_sequence_indices = deque()# contains ego_odom_idx
+        self.cooldown_indices = deque() # contains ego_odom_idx
+        self.ego_odom_id2last_published_timestamp=dict() # for checking last published timestap.
+
+        self.prev_ego_pos = None
+        self.prev_ego_odom_msg = None
+        self.perv_objects_msg = None
+        self.prev_traffic_signals_msg = None
+
+
+        self.stopwatch = StopWatch(self.args.verbose)  # for debug
+
+        # to make some data to accelerate computation
+        self.preprocess_data()
+
+        # start main timer callback
+        self.timer = self.create_timer(0.1, self.on_timer)
+
+        # kill perception process to avoid a conflict of the perception topics
+        self.timer_check_perception_process = self.create_timer(3.0, self.on_timer_kill_perception)
+
+        print("Start timer callback")
+
+    def preprocess_data(self):
+        # closest search with numpy data is much faster than usual
+        self.rosbag_ego_odom_data_numpy = np.array(
+            [
+                [data[1].pose.pose.position.x, data[1].pose.pose.position.y]
+                for data in self.rosbag_ego_odom_data
+            ]
+        )
+
+    def on_timer_kill_perception(self):
+        self.kill_online_perception_node()
+
+    def on_timer(self):
+        if self.args.verbose:
+            print("\n-- on_timer start --")
+        self.stopwatch.tic("total on_timer")
+
+        timestamp = self.get_clock().now()
+        timestamp_msg = timestamp.to_msg()
+
+        if self.args.detected_object:
+            pointcloud_msg = create_empty_pointcloud(timestamp_msg)
+            self.pointcloud_pub.publish(pointcloud_msg)
+
+        if not self.ego_pose:
+            print("No ego pose found.")
+            return
+        
+        ## Update reproduce list if ego_pos is moved.
+        if self.ego_pose is None \
+                or self.prev_ego_pos is None or \
+                ((self.ego_pose.position.x - self.prev_ego_pos.position.x)**2 + \
+                 (self.ego_pose.position.y - self.prev_ego_pos.position.y)**2) > dist_eps**2:
+
+            # find the nearest ego odom by simulation observation
+            self.stopwatch.tic("find_nearest_ego_odom_by_observation")
+            nearst_ego_odom_ind = self.find_nearest_ego_odom_index(self.ego_pose)
+            nearst_ego_odom_pos = self.rosbag_ego_odom_data[nearst_ego_odom_ind][1].pose.pose
+            self.stopwatch.toc("find_nearest_ego_odom_by_observation")
+
+            # find a list of ego odoms around the nearst_ego_odom_pos. 
+            self.stopwatch.tic("find_nearby_ego_odom_indies")
+            ego_odom_indices = self.find_nearby_ego_odom_indies(nearst_ego_odom_pos, 
+                                                                self.rosbag_ego_odom_seach_radius)
+            self.stopwatch.toc("find_nearby_ego_odom_indies")
+
+            # update reproduce_sequence with those data not in cooldown list.
+            while self.cooldown_indices:
+                last_timestamp = self.ego_odom_id2last_published_timestamp[self.cooldown_indices[0]]
+                if (timestamp - last_timestamp).nanoseconds/1e9 > self.reproduce_cooldown:
+                    self.cooldown_indices.popleft()
+                else:
+                    break
+
+
+            self.stopwatch.tic("update reproduce_sequence")
+            self.reproduce_sequence_indices = deque(
+                [idx for idx in ego_odom_indices 
+                 if idx not in self.cooldown_indices])
+            self.stopwatch.toc("update reproduce_sequence")
+
+        self.prev_ego_pos = self.ego_pose
+        
+        ## get data to publish
+        repeat_trigger = len(self.reproduce_sequence_indices) == 0
+        if not repeat_trigger:# pop data from reproduce_sequence if sequence is not empty.
+            ego_odom_idx = self.reproduce_sequence_indices.popleft()
+            pose_timestamp, ego_odom_msg = self.rosbag_ego_odom_data[ego_odom_idx]
+            # extract message by the nearest ego odom timestamp
+            self.stopwatch.tic("find_topics_by_timestamp")
+            objects_msg, traffic_signals_msg = self.find_topics_by_timestamp(pose_timestamp)
+            self.stopwatch.toc("find_topics_by_timestamp")
+
+        else:#get perv data to publish if reproduce_sequence is empty.
+            ego_odom_msg = self.prev_ego_odom_msg
+            objects_msg = self.perv_objects_msg
+            traffic_signals_msg = self.prev_traffic_signals_msg
+
+        ## transform and publish current data.
+        self.stopwatch.tic("transform and publish")
+
+        # ego odom
+        self.recorded_ego_pub.publish(ego_odom_msg)
+
+        # objects
+        if objects_msg:
+            objects_msg.header.stamp = timestamp_msg
+            if self.args.detected_object:
+                # copy the messages
+                self.stopwatch.tic("message deepcopy")
+                objects_msg_copied = pickle.loads(pickle.dumps(objects_msg))  # this is x5 faster than deepcopy
+                self.stopwatch.toc("message deepcopy")
+
+                log_ego_pose = ego_odom_msg.pose.pose
+                translate_objects_coordinate(self.ego_pose, log_ego_pose, objects_msg_copied)
+            else:
+                objects_msg_copied = objects_msg
+            self.objects_pub.publish(objects_msg_copied)
+
+
+        # traffic signals
+        if traffic_signals_msg:
+            traffic_signals_msg.stamp = timestamp_msg
+            self.traffic_signals_pub.publish(traffic_signals_msg)
+            self.prev_traffic_signals_msg = traffic_signals_msg
+        elif self.prev_traffic_signals_msg:
+            self.prev_traffic_signals_msg.stamp = timestamp_msg
+            self.traffic_signals_pub.publish(self.prev_traffic_signals_msg)
+        self.stopwatch.toc("transform and publish")
+
+        if not repeat_trigger:
+            # save data for repeate pubulication.
+            self.prev_ego_odom_msg = ego_odom_msg
+            self.perv_objects_msg = objects_msg if objects_msg is not None else self.perv_objects_msg
+            self.prev_traffic_signals_msg = traffic_signals_msg if traffic_signals_msg is not None else self.prev_traffic_signals_msg
+
+            # update cooldown info.
+            self.ego_odom_id2last_published_timestamp[ego_odom_idx] = timestamp
+            self.cooldown_indices.append(ego_odom_idx)
+
+            self.stopwatch.toc("total on_timer")
+
+    def find_nearest_ego_odom_index(self, ego_pose):
+        # nearest search with numpy format is much (~ x100) faster than regular for loop
+        self_pose = np.array([ego_pose.position.x, ego_pose.position.y])
+        dists_squared = np.sum((self.rosbag_ego_odom_data_numpy - self_pose) ** 2, axis=1)
+        nearest_idx = np.argmin(dists_squared)
+        return nearest_idx
+    
+    def find_nearby_ego_odom_indies(self, ego_pose, search_radius: float):
+        '''
+        Find the indices of the ego odoms that are within a certain distance of the ego_pose.
+        Inputs:
+            ego_pose: the ego pose to search around.
+            search_radius: the radius to search around the ego_pose.'''
+        self_pose = np.array([ego_pose.position.x, ego_pose.position.y])
+        dists_squared = np.sum((self.rosbag_ego_odom_data_numpy - self_pose) ** 2, axis=1)
+        nearby_indices = np.where(dists_squared <= search_radius**2)[0]
+
+        return nearby_indices
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-b", "--bag", help="rosbag", default=None)
+    parser.add_argument(
+        "-d", "--detected-object", help="publish detected object", action="store_true"
+    )
+    parser.add_argument(
+        "-t", "--tracked-object", help="publish tracked object", action="store_true"
+    )
+    parser.add_argument(
+        "-f", "--rosbag-format", help="rosbag data format (default is db3)", default="db3"
+    )
+    parser.add_argument(
+        "-v", "--verbose", help="output debug data", action="store_true", default=False
+    )
+    parser.add_argument(
+        "-r", "--seach-radius", help="the search radius for searching rosbag's ego odom messages around the nearest ego odom pose (default is 1 meters)", type=float, default=1.0
+    )
+    parser.add_argument(
+        "-c", "--reproduce-cooldown", help="The cooldown time for republishing published messages (default is 40.0 seconds)", type=float, default=40.0
+    )
+    args = parser.parse_args()
+
+    rclpy.init()
+    node = PerceptionReproducerV2(args)
+
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()